A harsh reality: In terms of knowledge coverage, execution stamina, and standardized output, humans are already struggling to compete directly with AI. In other words, for jobs that are purely knowledge + skill-based, humans can be completely replaced by AI, and AI has almost zero fatigue costs. Just like Jack Dorsey @jack mentioned in this post: In the future, organizations will only need three types of roles—individual experts, resource providers, and multi-dimensional talents. All other coordination work will be handled by systems. I totally agree with this perspective because AI has essentially entered the elimination stage. Right now, there are only three areas worth diving deeper into: 1⃣ How to verify the results delivered by AI The strongest aspect of AI is generation. If you lack the ability to quickly identify logical flaws, information biases, or conclusion risks, then using AI is just amplifying errors. 2⃣ How to better orchestrate AI to work Many people only treat it as an upgraded search box, far from achieving a qualitative leap. The real efficiency gap lies in whether you can break down tasks, push them forward concurrently, and provide sufficient context to make AI outputs align more closely with your true intentions. 3⃣ How to better consolidate your own methodology This is actually the easiest point to overlook, but it’s also the most critical for building an advantage. Good prompt structures, task workflows, validation frameworks, and review habits—these must be consolidated and turned into your own methodological assets. Models can keep getting stronger, tools are public, but methods are private.